Abstract
A notable increase in research related to geospatial vulnerability assessment of urban floods is observed, primarily driven by the advances in exponential technologies and increasing frequency and severity of such events. Several studies have been attempted in Indian contexts for local-level risk mitigation and preparedness and informed decision-making with a limited scope due to unavailability of granular data at various levels as required. In response to this gap, current study examines the potential of top-down and bottom-up geospatial vulnerability assessment approaches for urban floods in Patna City of Bihar state, India. A qualitative articulation is used to identify and characterise a set of critical indicators and attributes relevant to the local vulnerability assessment of urban floods specific to the study area. Principal Component Analysis (PCA) is applied for dimension reduction and assigning weights to indicators, and Composite Vulnerability Indices (CVIs) at Ward and Household (HH) levels are developed using multicriteria decision analysis. The results show over a fifty percent variation in the vulnerability profiles of households using ward-level (top-down) and HH-level (bottom-up) approaches. Moreover, a narrower range in the z-score of HH-level CVI (−1.26 to 2.60) over ward-level CVI (−3.95 to 2.49) from sensitivity analysis indicates the robustness of the former over the latter. The findings reveal a potential risk of maladaptation when employing a ward-level approach to vulnerability assessment as a policy instrument. The study also analytically validates the significance of localising indicators in regional and global frameworks for achieving efficient decision-making toward sustainability targets.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.